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Found 9 papers, 6 papers with code
Novel community data in ecology -- properties and prospects
New technologies for acquiring biological information such as eDNA, acoustic or optical sensors, make it possible to generate spatial community observations at unprecedented scales.
Can predictive models be used for causal inference?
Here, we show that this trade-off between explanation and prediction is not as deep and fundamental as expected.
cito: An R package for training neural networks using torch
Most current deep learning (DL) applications rely on one of the major deep learning frameworks, in particular Torch or TensorFlow, to build and train DNN.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
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The evidence contained in the P-value is context dependent
In a recent opinion article, Muff et al. recapitulate well-known objections to the Neyman-Pearson Null-Hypothesis Significance Testing (NHST) framework and call for reforming our practices in statistical reporting.
Machine Learning and Deep Learning -- A review for Ecologists
Finally, we summarize emerging trends such as scientific and causal ML, explainable AI, and responsible AI that may significantly impact ecological data analysis in the future.
A new method for faster and more accurate inference of species associations from big community data
Our sjSDM approach makes the analysis of JSDMs to large community datasets with hundreds or thousands of species possible, substantially extending the applicability of JSDMs in ecology.
Inferring species interactions using Granger causality and convergent cross mapping
Our results therefore imply that Granger causality, even in its linear MAR($p$) formulation, is a valid method for inferring interactions in nonlinear ecological networks; using GC or CCM (or both) can instead be decided based on the aims and specifics of the analysis.
Machine learning algorithms to infer trait-matching and predict species interactions in ecological networks
Using simulated and real data, we contrast conventional generalized linear models (GLM) with more flexible Machine Learning (ML) models (Random Forest, Boosted Regression Trees, Deep Neural Networks, Convolutional Neural Networks, Support Vector Machines, naive Bayes, and k-Nearest-Neighbor), testing their ability to predict species interactions based on traits, and infer trait combinations causally responsible for species interactions.
An Extended Empirical Saddlepoint Approximation for Intractable Likelihoods
The challenges posed by complex stochastic models used in computational ecology, biology and genetics have stimulated the development of approximate approaches to statistical inference.
Methodology Applications
